Term

Growth hacking

An approach to growth that rapidly tests cheap, creative ideas across marketing, product and data and scales the ones that work. It is typical of startups with limited budgets.

Updated

What it means

Growth hacking is an approach to growth that rapidly tests cheap, creative ideas across marketing, product and data and scales the ones that work. The term emerged among startups with limited budgets that needed to grow fast without expensive campaigns.

It typically blends marketing with product: growth is sought not only in advertising but also in features that encourage users to invite others, simpler sign-up or better onboarding. A well-known example is a referral programme that rewards both sides.

Growth hacking relies on metrics and experiments, often using the AARRR framework of acquisition, activation, retention, referral and revenue. Its principles are close to the lean startup method: form a hypothesis, test it and learn from the result.

How it works in practice

The team keeps a backlog of ideas, scores them by expected impact, confidence and effort, and launches several experiments a week. Results are measured with A/B tests or cohort comparisons, and successful changes are made permanent.

It is important to track customer acquisition cost, CAC, and customer value over time. Cheap growth that brings users who quickly leave adds no value, so experiments are judged on retention and revenue, not just sign-ups.

For smaller businesses, growth hacking can mean simple steps: optimising the highest-traffic page, partnering with businesses that reach the same customers, automated e-mails after sign-up or a simpler checkout.

What to watch out for

Aggressive tactics such as collecting contacts without consent, fake reviews, artificial urgency or automatically adding people to lists can breach the GDPR and consumer protection rules. Short-term growth can then end in fines and lost trust.

Experiments without enough data lead to wrong conclusions. With low traffic, test bigger changes and evaluate results over longer periods, or the team will roll out changes based on random fluctuations.

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